TF-RNF: A novel term weighting scheme for sports video classification

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dc.contributor.authorPrisana Mutchima
dc.contributor.authorParinya Sanguansat
dc.contributor.correspondenceP. Mutchima; Department of Information Technology, Suan Dusit Rajabhat University, Thailand; email: prisanut@hotmail.com
dc.date.accessioned2025-03-10T07:37:40Z
dc.date.available2025-03-10T07:37:40Z
dc.date.issued2012
dc.description.abstractDetermination of content importance is very important in achieving high quality classification. Term weighting schemes in text classification will be applied to classify videos by measuring importance of video contents. In other words, a video sequence can be treated as a document, and frames of a video are considered as words or terms which identify contents of a video. And to enhance the efficiency of video classification, this paper proposes a novel term weighting scheme, called the Term Frequency - Relevance and Non-relevance Frequency (TF-RNF) weighting. This technique can filter both relevant and non-relevant contents so as to reduce classification errors. Empirical evaluations of results show that the proposed technique significantly outperforms traditional techniques in sports video classification. © 2012 IEEE.
dc.identifier.citation2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
dc.identifier.doi10.1109/ICSPCC.2012.6335651
dc.identifier.isbn978-146732193-8
dc.identifier.scopus2-s2.0-84869444133
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4976
dc.languageEnglish
dc.rights.holderScopus
dc.subjectsports video
dc.subjectterm weighting
dc.subjectTF-RNF
dc.subjectVideo classification
dc.titleTF-RNF: A novel term weighting scheme for sports video classification
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84869444133&doi=10.1109%2fICSPCC.2012.6335651&partnerID=40&md5=81dca196fd7b3f9d599cbc0f3e16d723
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oaire.citation.startPage244
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